Systems and methods for detecting human presence near a transaction kiosk

ABSTRACT

Disclosed embodiments may include a system that may receive first level authentication data from a first user, identify a first user device associated with the first user, and determine whether a current location of the first user device is within a predetermined proximity of a first computing device. In response to the determination, the system may detect one or more objects within the predetermined proximity of the first computing device using the one or more positional sensors. The system may determine that at least one of the one or more objects is associated with a human, and in response, trigger a security measure. The system may transmit an indication of the triggered security measure to the first computing device, and may transmit instructions to the first user device configured to cause the first user device to provide an alert to the first user.

FIELD

The disclosed technology relates to systems and methods for detectingthe presence of a human near a transaction kiosk. In particular, thedisclosed technology relates to determining when a potentialeavesdropper is within a predetermined proximity of an in-usetransaction kiosk and alerting an authorized user of the presence of thepotential eavesdropper.

BACKGROUND

Customers often visit automated teller machines (ATMs) and othertransaction kiosks located in high traffic areas. While high trafficareas tend to be convenient locations for ATMs and transaction kiosks,customers accessing an ATM or transaction kiosk in a high traffic areapose the risk of inadvertently exposing their private financialinformation (e.g., password, PIN, account balance, etc.) to a potentialeavesdropper while interacting with the ATM or transaction kiosk.Current practices do not provide a way to dynamically detect thepresence of a potential eavesdropper during an ATM or kiosk transactionand alert the user of the presence of the potential eavesdropper.

Accordingly, there is a need for improving the security of the ATM andtransaction kiosk experience to detect the presence of an eavesdropperand alert a customer of their presence. Embodiments of the presentdisclosure are directed to this and other considerations.

SUMMARY

Disclosed embodiments may include a system for alerting a customer ofthe presence of a potential eavesdropper during an ATM or kiosktransaction. The system may include one or more processors, one or morepositional sensors configured to communicate with the one or moreprocessors, and a memory in communication with the one or moreprocessors and storing instructions that are configured to cause thesystem to perform the steps of a method. For example, the system mayreceive first level authentication data from a first user and identify afirst user device associated with the first user based on the firstlevel authentication data. The system may determine whether a currentlocation of the first user device is within a predetermined proximity ofa first computing device. When the system determines that the currentlocation of the first user device is within the predetermined proximityof the first computing device, the system may detect one or more objectswithin the predetermined proximity of the first computing device usingone or more positional sensors. The system may determine that at leastone of the one or more objects is associated with a human, and inresponse to the determination, trigger a security measure, and transmitan indication of the triggered security measure to the first computingdevice.

Disclosed embodiments, may include a system for alerting a customer ofthe presence of a potential eavesdropper during an ATM or kiosktransaction. The system may include one or more processors, and a memoryin communication with the one or more processors and storinginstructions that, when executed by the one or more processors areconfigured to cause the system to perform the steps of a method. Forexample, the system may receive first level authentication data form afirst user, and identify a first user device associated with the firstuser based on the first level authentication data. The system maydetermine whether a current location of the first user device is withina predefined area proximate a first computing device. In response todetermining that the current location of the first user device is withinthe predefined area proximate the first computing device, the system mayreceive object data associated with one or more objects within thepredefined area. The system may determine that at least one of the oneor more objects is associated with a human and, responsive to thedetermination, trigger a security measure.

Disclosed embodiments may include a method for alerting a customer ofthe presence of a potential eavesdropper during an ATM or kiosktransaction. The method may include receiving first level authenticationdata from a first user, and identifying a first user device associatedwith the first user based on the first level authentication data. Themethod may include determining whether a current location of the firstuser device is within a predetermined perimeter around a first computingdevice. In response to determining that the current location of thefirst user device is within the predetermined perimeter around the firstcomputing device, the method may include detecting, with one or morepositional sensors positioned within the predetermined perimeter aroundthe first computing device, one or more objects within the predeterminedperimeter around the first computing device. The method may includedetermining that at least one of the one or more objects is associatedwith a human, and in response to the determination, triggering asecurity measure. The method may include transmitting an indication ofthe triggered security measure to the first computing device andtransmitting instructions to the first user device configured to causethe first user device to provide an audible or vibrational alert to thefirst user.

Further features of the disclosed design, and the advantages offeredthereby, are explained in greater detail hereinafter with reference tospecific embodiments illustrated in the accompanying drawings, whereinlike elements are indicated by like reference designators.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and which illustrate variousimplementations, aspects, and principles of the disclosed technology. Inthe drawings:

FIG. 1 is a block diagram of an example system that may be used to alerta customer of the presence of a potential eavesdropper, according to anexample implementation of the disclosed technology.

FIG. 2 is a block diagram of an example proximity detection device usedto detect the presence of a potential eavesdropper, according to anexample implementation of the disclosed technology.

FIG. 3 is a flow diagram illustrating an exemplary method for triggeringa security measure in response to detecting one or more objectsassociated with a human, in accordance with certain embodiments of thedisclosed technology.

FIG. 4 is a flow diagram illustrating an exemplary method for triggeringa security measure in response to detecting one or more objectsassociated with a human, in accordance with certain embodiments of thedisclosed technology.

FIG. 5 is a flow diagram illustrating an exemplary method for confirmingthat the current location of the first user device is within thepredetermined proximity of the location of the first computing device,in accordance with certain embodiments of the disclosed technology.

FIG. 6 is a flow diagram illustrating an exemplary method for confirmingthat the current location of the first user device is within thepredetermined proximity of the location of the computing device, inaccordance with certain embodiments of the disclosed technology.

FIG. 7 is a flow diagram illustrating an exemplary method fordetermining that a second object is associated with the presence of ahuman, in accordance with certain embodiments of the disclosedtechnology.

DETAILED DESCRIPTION

Examples of the present disclosure relate generally to determining whena potential eavesdropper is within a predetermined proximity of anin-use transaction kiosk or ATM and alerting an authorized user of thepresence of the potential eavesdropper. The systems and methodsdescribed herein are necessarily rooted in computer technology as theyrelate to dynamically detecting one or more objects within apredetermined proximity of a transaction kiosk, and determining that atleast one of the one or more objects is a human after confirming thepresence of a first user interacting with the transaction kiosk. In someinstances, the system utilizes a machine learning model to determinethat at least one of the detecting objects is a human. Machine learningmodels are a unique computer technology that involves training themodels to complete a task, such as determining a detected object is ahuman, by feeding the models with labeled data sets so the machinelearning models learn how to rank or score the training data sets andapply the same ranking or scoring to unlabeled data sets. Importantly,examples of the present disclosure improve the speed with whichcomputers can identify a detected object as a human potentialeavesdropper and trigger a security measure based on the presence of thepotential eavesdropper based on the score the machine learning modelgenerates. According to some embodiments, the system may receive firstlevel authentication data from a first user, identify a first userdevice associated with the first user, and determine whether the currentlocation of the first user device is within a predetermined proximity toa transaction kiosk. The system may detect one or more objects withinthe predetermined proximity of the transaction kiosk using positionalsensors. The system may collect positional fingerprint data associatedwith each of the one or more detected objects while the transactionkiosk is in use by the first user. When the system determines that thecollected positional fingerprint data matches stored fingerprint dataassociated with the presence of a human, the system may automaticallytrigger a security measure to alert the first user of the presence of apotential eavesdropper.

Some implementations of the disclosed technology will be described morefully with reference to the accompanying drawings. This disclosedtechnology may, however, be embodied in many different forms and shouldnot be construed as limited to the implementations set forth herein. Thecomponents described hereinafter as making up various elements of thedisclosed technology are intended to be illustrative and notrestrictive. Many suitable components that would perform the same orsimilar functions as components described herein are intended to beembraced within the scope of the disclosed electronic devices andmethods.

Reference will now be made in detail to example embodiments of thedisclosed technology that are illustrated in the accompanying drawingsand disclosed herein. Wherever convenient, the same reference numberswill be used throughout the drawings to refer to the same or like parts.

FIG. 1 is a block diagram of an example system that may be used alert acustomer of the presence of a potential eavesdropper, according to anexample implementation of the disclosed technology. The components andarrangements shown in FIG. 1 are not intended to limit the disclosedembodiments as the components used to implement the disclosed processesand features may vary. As shown, proximity detection system 108 mayinteract with a user device 102 and a financial service provider system140 via a network 106. In certain example implementations, the proximitydetection system 108 may include a local network 112, a proximitydetection device 120, a computer interface 130, and positional sensors122.

In some embodiments, a user may operate the user device 102. The userdevice 102 can include one or more of a mobile device, smart phone,general purpose computer, tablet computer, laptop computer, telephone,PSTN landline, smart wearable device, voice command device, other mobilecomputing device, or any other device capable of communicating with thenetwork 106 and ultimately communicating with one or more components ofthe proximity detection system 108. In some embodiments, the user device102 may include or incorporate electronic communication devices forhearing or vision impaired users.

Customers may include individuals such as, for example, subscribers,clients, prospective clients, or customers of an entity associated withan organization, such as individuals who have obtained, will obtain, ormay obtain a product, service, or consultation from or conduct atransaction in relation to an entity associated with the proximitydetection system 108. According to some embodiments, the user device 102may include an environmental sensor for obtaining audio or visual data,such as a microphone and/or digital camera, a geographic location sensorfor determining the location of the device, an input/output device suchas a transceiver for sending and receiving data, a display fordisplaying digital images, one or more processors, and a memory incommunication with the one or more processors.

The network 106 may be of any suitable type, including individualconnections via the internet such as cellular or WiFi networks. In someembodiments, the network 106 may connect terminals, services, and mobiledevices using direct connections such as radio-frequency identification(RFID), near-field communication (NFC), Bluetooth™, low-energyBluetooth™ (BLE), WiFi™, ZigBee™, ambient backscatter communications(ABC) protocols, USB, WAN, or LAN. Because the information transmittedmay be personal or confidential, security concerns may dictate one ormore of these types of connections be encrypted or otherwise secured. Insome embodiments, however, the information being transmitted may be lesspersonal, and therefore the network connections may be selected forconvenience over security.

The network 106 may include any type of computer networking arrangementused to exchange data. For example, the network 106 may be the Internet,a private data network, virtual private network using a public network,and/or other suitable connection(s) that enable(s) components in thesystem 100 environment to send and receive information between thecomponents of the system 100. The network 106 may also include a publicswitched telephone network (“PSTN”) and/or a wireless network.

The proximity detection system 108 may be associated with and optionallycontrolled by one or more entities such as a business, corporation,individual, partnership, or any other entity that provides one or moreof goods, services, and consultations to individuals such as customers.The proximity detection system 108 may include one or more servers andcomputer systems for performing one or more functions associated withproducts and/or services that the organization provides.

The local network 112 may include any type of computer networkingarrangement used to exchange data in a localized area, such as WiFi,Bluetooth™ Ethernet, and other suitable network connections that enablecomponents of the proximity detection system 108 to interact with oneanother and to connect to the network 106 for interacting withcomponents in the system 100 environment. In some embodiments, the localnetwork 112 may include an interface for communicating with or linkingto the network 106. In other embodiments, certain components of theproximity detection system 108 may communicate via the network 106,without a separate local network 112.

In accordance with certain example implementations of the disclosedtechnology, the proximity detection system 108 may include one or morecomputer systems configured to compile data from a plurality of sourcesincluding the proximity detection device 120, computer interface 130,and/or the positional sensors 122. The proximity detection device 120may correlate compiled data, analyze the compiled data, arrange thecompiled data, generate derived data based on the compiled data, andstore the compiled and derived data in a database, such as the database260, as described more fully with respect to FIG. 2 . According to someembodiments, the database 260 may be a database associated with anorganization and/or a related entity that stores a variety ofinformation relating to customers, transactions, ATM, and businessoperations.

The positional sensors 122 may include sensors located within apredetermined proximity of the computer interface 130 and configured tocollect positional data associated with a user and proximity dataassociated with one or more objects detected within a predeterminedproximity of the computer interface 130. For example, the positionalsensors 122 may include one or more of a LIDAR positional sensor,ultrasound positional sensor, capacitive positional sensor, resistivepositional sensor, and radio-frequency positional sensor. The datacollected by positional sensors 122 may be stored as a fingerprint thatthe system 100 (e.g., proximity detection device 120) may utilize todetect one or more objects and determine whether at least one of the oneor more detected objects correspond to a human. For example, a LIDARpositional sensor may be used to create a 3-dimensional scan of the areasurrounding the computer interface 130. Newly collected LIDAR positionaldata may be compared to stored LIDAR positional data to identify thepresence of a human. The ultrasound positional sensor may be utilized ina similar manner. The capacitive positional sensor may be configured tomeasure a change in the electrical property known as capacitance and maybe placed in the floor or ground directly proximate the computerinterface 130 and may additionally include a reference target positionedabove the capacitive sensor. When a human or other entity walks onto theground over the capacitive sensor and between the capacitive sensor andthe reference target, the capacitive sensor may detect a change incapacitance due to the entity or human having a different dielectricconstant than air. The resistive positional sensor may also be installedinto the floor or ground directly proximate the computer interface 130and may employ a small electrical current to detect a change inresistive properties of the ground or floor surface as a human or otherentity walks over the surface. Finally, a radio-frequency positionalsensor may detect a change in a radio-frequency profile in a given areawhen an object (e.g., a human or other entity) enters a given area andchanges a background radio-frequency reading.

FIG. 2 is a block diagram of an example proximity detection device 120used to detect the presence of a potential eavesdropper within apredetermined proximity of an ATM or transaction kiosk, according to anexample implementation of the disclosed technology. According to someembodiments, the user device 102, computer interface 130, and financialservice provider system 140, as described with respect to FIG. 1 , mayhave a similar structure and components that are similar to thosedescribed with respect to proximity detection device 120 shown in FIG. 2. As shown, the proximity detection device 120 may include a processor210, an input/output (“I/O”) device 220, a memory 230 containing anoperating system (“OS”) 240 and a program 250. In certain exampleimplementations, the proximity detection device 120 may be a singleserver or may be configured as a distributed computer system includingmultiple servers or computers that interoperate to perform one or moreof the processes and functionalities associated with the disclosedembodiments. In some embodiments proximity detection device 120 may beone or more servers from a serverless or scaling server system. In someembodiments, the proximity detection device 120 may further include aperipheral interface, a transceiver, a mobile network interface incommunication with the processor 210, a bus configured to facilitatecommunication between the various components of the proximity detectiondevice 120, and a power source configured to power one or morecomponents of the proximity detection device 120.

A peripheral interface, for example, may include the hardware, firmwareand/or software that enable(s) communication with various peripheraldevices, such as media drives (e.g., magnetic disk, solid state, oroptical disk drives), other processing devices, or any other inputsource used in connection with the disclosed technology. In someembodiments, a peripheral interface may include a serial port, aparallel port, a general-purpose input and output (GPIO) port, a gameport, a universal serial bus (USB), a micro-USB port, a high definitionmultimedia (HDMI) port, a video port, an audio port, a Bluetooth™ port,a near-field communication (NFC) port, another like communicationinterface, or any combination thereof.

In some embodiments, a transceiver may be configured to communicate withcompatible devices and ID tags when they are within a predeterminedrange. A transceiver may be compatible with one or more of:radio-frequency identification (RFID), near-field communication (NFC),Bluetooth™, low-energy Bluetooth™ (BLE), WiFi™, ZigBee™, ambientbackscatter communications (ABC) protocols or similar technologies.

A mobile network interface may provide access to a cellular network, theInternet, or another wide-area or local area network. In someembodiments, a mobile network interface may include hardware, firmware,and/or software that allow(s) the processor(s) 210 to communicate withother devices via wired or wireless networks, whether local or widearea, private or public, as known in the art. A power source may beconfigured to provide an appropriate alternating current (AC) or directcurrent (DC) to power components.

The processor 210 may include one or more of a microprocessor,microcontroller, digital signal processor, co-processor or the like orcombinations thereof capable of executing stored instructions andoperating upon stored data. The memory 230 may include, in someimplementations, one or more suitable types of memory (e.g. such asvolatile or non-volatile memory, random access memory (RAM), read onlymemory (ROM), programmable read-only memory (PROM), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), magnetic disks, optical disks,floppy disks, hard disks, removable cartridges, flash memory, aredundant array of independent disks (RAID), and the like), for storingfiles including an operating system, application programs (including,for example, a web browser application, a widget or gadget engine, andor other applications, as necessary), executable instructions and data.In one embodiment, the processing techniques described herein may beimplemented as a combination of executable instructions and data storedwithin the memory 230.

The processor 210 may be one or more known processing devices, such as,but not limited to, a microprocessor from the Pentium™ familymanufactured by Intel™ or the Turion™ family manufactured by AMD™. Theprocessor 210 may constitute a single core or multiple core processorthat executes parallel processes simultaneously. For example, theprocessor 210 may be a single core processor that is configured withvirtual processing technologies. In certain embodiments, the processor210 may use logical processors to simultaneously execute and controlmultiple processes. The processor 210 may implement virtual machinetechnologies, or other similar known technologies to provide the abilityto execute, control, run, manipulate, store, etc. multiple softwareprocesses, applications, programs, etc. One of ordinary skill in the artwould understand that other types of processor arrangements could beimplemented that provide for the capabilities disclosed herein.

In accordance with certain example implementations of the disclosedtechnology, the proximity detection device 120 may include one or morestorage devices configured to store information used by the processor210 (or other components) to perform certain functions related to thedisclosed embodiments. In one example, the proximity detection device120 may include the memory 230 that includes instructions to enable theprocessor 210 to execute one or more applications, such as serverapplications, network communication processes, and any other type ofapplication or software known to be available on computer systems.Alternatively, the instructions, application programs, etc. may bestored in an external storage or available from a memory over a network.The one or more storage devices may be a volatile or non-volatile,magnetic, semiconductor, tape, optical, removable, non-removable, orother type of storage device or tangible computer-readable medium.

In one embodiment, the proximity detection device 120 may include amemory 230 that includes instructions that, when executed by theprocessor 210, perform one or more processes consistent with thefunctionalities disclosed herein. Methods, systems, and articles ofmanufacture consistent with disclosed embodiments are not limited toseparate programs or computers configured to perform dedicated tasks.For example, the proximity detection device 120 may include the memory230 that may include one or more programs 250 to perform one or morefunctions of the disclosed embodiments.

In certain example implementations, the program 250 may include arule-based platform 270 for determining whether an identified objectcorresponds to a human using a set of predefined rules. In someembodiments, the proximity detection device 120 may include a trainedmachine learning model 275 for determining whether an identified objectcorresponds to a human, in accordance with a model that may becontinuously or intermittently updated. Moreover, the processor 210 mayexecute one or more programs 250 located remotely from the system 100(such as the system shown in FIG. 1 ). For example, the system 100 mayaccess one or more remote programs 250 (such as the rule-based platform270 or the trained machine learning model 275), that, when executed,perform functions related to disclosed embodiments.

The memory 230 may include one or more memory devices that store dataand instructions used to perform one or more features of the disclosedembodiments. The memory 230 may also include any combination of one ormore databases controlled by memory controller devices (e.g., server(s),etc.) or software, such as document management systems, Microsoft™ SQLdatabases, SharePoint™ databases, Oracle™ databases, Sybase™ databases,or other relational or non-relational databases. The memory 230 mayinclude software components that, when executed by the processor 210,perform one or more processes consistent with the disclosed embodiments.In some embodiments, the memory 230 may include a database 260 forstoring data related to training datasets for determining whetherobjects detected are human to enable the proximity detection device 120to perform one or more of the processes and functionalities associatedwith the disclosed embodiments.

The database 260 may include stored data relating to training datasets(e.g., labeled training datasets that are configured to train theproximity detection device 120 to determine whether a detected object isa human), positional fingerprint data (for identifying a user of thecomputer interface 130 and/or identifying the presence of a humaneavesdropper), and historical activity data associated with a user of atransaction kiosk/ATM (e.g., computer interface 130). According to someembodiments, the functions provided by the database 460 may also beprovided by a database that is external to the proximity detectiondevice 120.

The proximity detection device 120 may also be communicatively connectedto one or more memory devices (e.g., databases) locally or through anetwork. The remote memory devices may be configured to storeinformation and may be accessed and/or managed by the proximitydetection device 120. By way of example, the remote memory devices maybe document management systems, Microsoft™ SQL database, SharePoint™databases, Oracle™ databases, Sybase™ databases, or other relational ornon-relational databases. Systems and methods consistent with disclosedembodiments, however, are not limited to separate databases or even tothe use of a database.

The proximity detection device 120 may also include one or more I/Odevices 220 that may comprise one or more interfaces for receivingsignals or input from devices and providing signals or output to one ormore devices that allow data to be received and/or transmitted by theproximity detection device 120. For example, the proximity detectiondevice 120 may include interface components, which may provideinterfaces to one or more input devices, such as one or more keyboards,mouse devices, touch screens, track pads, trackballs, scroll wheels,digital cameras, microphones, sensors, and the like, that enable theproximity detection device 120 to receive data from a user (such as, forexample, via the user device 102), computer interface 130, and/orpositional sensors 122.

In example embodiments of the disclosed technology, the proximitydetection device 120 may include any number of hardware and/or softwareapplications that are executed to facilitate any of the operations. Theone or more I/O interfaces may be utilized to receive or collect dataand/or user instructions from a wide variety of input devices. Receiveddata may be processed by one or more computer processors as desired invarious implementations of the disclosed technology and/or stored in oneor more memory devices.

While the proximity detection device 120 has been described as one formfor implementing the techniques described herein, other, functionallyequivalent, techniques may be employed. For example, some or all of thefunctionality implemented via executable instructions may also beimplemented using firmware and/or hardware devices such as applicationspecific integrated circuits (ASICs), programmable logic arrays, statemachines, etc. Furthermore, other implementations of the proximitydetection device 120 may include a greater or lesser number ofcomponents than those illustrated.

FIG. 3 is a flow diagram illustrating an exemplary method for triggeringa security measure in response to detecting one or more objectsassociated with a human, in accordance with certain embodiments of thedisclosed technology. The steps of method 300 may be performed by one ormore components of the system 100 (e.g., proximity detection device 120or computer interface 530 of proximity detection system 108 and/or userdevice 102 and financial service provider system 140).

In block 305, the system (e.g., computer interface 130) may receivefirst level authentication data from a first user. For example, the usermay input a username and password, security PIN, or may swipe a cardassociated with an account of the first user.

In block 310, the system may identify a first user device based on thefirst level authentication data. For example, the computer interface 130may query a financial service provider 140 to determine a mobile device(e.g., user device 102) associated with the first user. Financialservice provider 140 may have a record of a phone number associated witha mobile device of the first user. Additionally, financial serviceprovider 140 may provide an application that may be installed on theuser device 102 that may allow financial service provider to determinecertain information associated with the user device 102, such as GPSlocation data.

In decision block 315, the system may determine whether the currentlocation of the first user device is within a predetermined proximity ofthe first computing device (e.g., computer interface 130). For example,one or more components of the system (e.g., proximity detection device120 and/or computer interface 130) may query the financial serviceprovider system 140 to determine a current location of the user device102. When the current location of the first user device (user device102) is not within the predetermined proximity of the first computingdevice (computer interface 130), the method may end.

When the current location of user device 102 is within the predeterminedproximity of the computer interface 130, the system (e.g., proximitydetection device 120) may detect one or more objects within thepredetermined proximity of the first computing device (computerinterface 130) in block 320. For example, the system may use one or morepositional sensors (e.g., positional sensors 122) to detect one or moreobjects within the predetermined proximity of the computer interface130. According to some embodiments, the user of user device 102 mayindicate whether one or more of the detected objects corresponds to anentity that is not an eavesdropper. For example, if the user isaccompanied by a child, a pet, or friends, the user may indicate theirpresence as verified objects within an application provided on the userdevice 102. Accordingly, once these objects have been identified as notan eavesdropper by the user using user device 102, the system may nottrigger a security measure even based on the presence of the verifiedobjects.

In decision block 325, the system may determine whether one or moreobjects are associated with a human. When the system determines that oneor more objects are not associated with a human, the method may end. Forexample, the system (e.g., proximity detection system 120) may use thepositional sensors 122 (e.g., LIDAR, ultrasound, capacitive, resistive,radio-frequency, etc.) and one or more of machine learning model 275and/or rule-based platform 270 to determine whether the one or moreobjects are associated with a human by determining whether recordedpositional fingerprints match a stored fingerprint associated with thepresence of a human beyond a predetermined threshold of similarity.

When at least one of the one or more objects are determined to be ahuman, the system (e.g., proximity detection device 120) may trigger asecurity measure in step 330. For example, the triggered securitymeasure may include one or more of causing a display of computerinterface 130 to flash, causing the display of computer interface 130 totemporarily display an empty screen and/or causing the display ofcomputer interface 130 to identify a position associated with a presenceof the human within the predefined area. For example, the display mayindicate the presence of a human with a conspicuous arrow pointing inthe direction of the detected human.

In step 335, the system (e.g., proximity detection device 120) maytransmit an indication of the triggered security measure to the firstcomputing device (e.g., computer interface 130). In optional step 340,the system (e.g., proximity detection device 120) may transmitinstructions to the first user device (user device 102) that areconfigured to cause the first user device to provide an audible orvibrational alert to the first user.

FIG. 4 is a flow diagram illustrating an exemplary method for triggeringa security measure in response to detecting one or more objectsassociated with a human, in accordance with certain embodiments of thedisclosed technology. The steps of method 300 may be performed by one ormore components of the system 100 (e.g., proximity detection device 120or computer interface 530 of proximity detection system 108 and/or userdevice 102 and financial service provider system 140).

Method 400 of FIG. 4 is similar to method 300 of FIG. 3 . For example,blocks 405, 410, 415, 425, and 430 are substantially similar to blocks305, 310, 315, 325, and 330 and are not repeated herein for brevity. Inblock 420, the system may receive object data including one or moreobjects within the predefined area proximate the first computing device.For example, the object data may be received from one or more positionalsensors, which may include LIDAR sensors, ultrasound sensors, capacitivesensors, resistive sensors, radio-frequency sensors, etc.

FIG. 5 is a flow diagram illustrating an exemplary method for confirmingthat the current location of the first user device is within thepredetermined proximity of the location of the first computing device,in accordance with certain embodiments of the disclosed technology. Thesteps of method 500 may be performed by one or more components of thesystem 100 (e.g., proximity detection device 120 or computer interface530 of proximity detection system 108 and/or user device 102 andfinancial service provider system 140).

In block 505, in response to not determining that the current locationof the first user device (e.g. user device 102) is within thepredetermined proximity of the first computing device (e.g., computerinterface 130), the system may determine a first positional fingerprint.For example, the system may use one or more positional sensors (e.g.,positional sensors 122) to determine the first positional fingerprintfor the first user.

In block 510, the system may compare the first positional fingerprint toa stored fingerprint associated with the first user device.

In decision block 515, the system may determine whether the firstpositional fingerprint matches a stored positional fingerprintassociated with the first user. For example, the system may compare thefingerprints captured by the positional sensors 122 (e.g., LIDAR,resistive, capacitive, etc.) to the stored fingerprints associated witha stored profile for the first user (e.g., stored on database 260 and/orfinancial service provider 140).

When the first positional fingerprint matches the stored fingerprintbeyond a predetermined threshold, the system may confirm that thecurrent location of the first user device (e.g., user device 102) iswithin the predetermined proximity of the location of the firstcomputing device (e.g., computer interface 130) in block 520. When thefirst positional fingerprint does not match the stored fingerprintbeyond the predetermined threshold, the method may end.

FIG. 6 is a flow diagram illustrating an exemplary method for confirmingthat the current location of the first user device is within thepredetermined proximity of the location of the computing device, inaccordance with certain embodiments of the disclosed technology. Thesteps of method 600 may be performed by one or more components of thesystem 100 (e.g., proximity detection device 120 or computer interface530 of proximity detection system 108 and/or user device 102 andfinancial service provider system 140).

In block 605, responsive to not determining that the current location ofthe first user device is within the predetermined proximity of the firstcomputing device, the system may access historical activity dataassociated with the first user device. For example, historical activitydata may be stored on financial service provider system 140 and thehistorical activity data may be queried by one of computer interface 130and/or proximity detection device 120. Historical activity data may bedata indicative of times, dates, and patterns of use of a respectiveATM, kiosk, and/or computer interface.

In decision block 610, the system may determine whether current firstuser activity data matches stored historical activity data. Currentfirst user activity data may be data may be determined in response toreceiving first level authentication from a first user as described withrespect to step 305 in FIG. 3 .

When the current first user activity data matches historical activitydata beyond a predetermined threshold of similarity, the system mayconfirm that the current location of the first user is within thepredetermined proximity of the first computing device (e.g., computerinterface 130) in block 615. When the current first user activity datadoes not match historical activity data beyond the predeterminedthreshold of similarity, the method may end.

FIG. 7 is a flow diagram illustrating an exemplary method fordetermining that a second object is associated with the presence of ahuman, in accordance with certain embodiments of the disclosedtechnology. The steps of method 700 may be performed by one or morecomponents of the system 100 (e.g., proximity detection device 120 orcomputer interface 530 of proximity detection system 108 and/or userdevice 102 and financial service provider system 140).

In block 705, the system may determine a second positional fingerprintfor each of the one or more detected objects. The second positionalfingerprint may be collected using one or more positional sensors (e.g.,positional sensors 122), as described with respect to FIG. 1 . Thesecond positional fingerprint may indicate the presence, or absence, ofone or more humans within the predetermined proximity of computerinterface 130.

In block 710, the system may compare the second positionalfingerprint(s) to a stored second fingerprint associated with a presenceof a human. For example, the stored second fingerprint may be stored ondatabase 260 and/or on financial service provider system 140.

In decision block 715, the system may determine whether the secondpositional fingerprint(s) matches the second stored fingerprint beyond apredetermined threshold of similarity. In response to the secondpositional fingerprint(s) matching the second stored fingerprint beyondthe predetermined threshold of similarity, the system may determine thata second object is associated with the presence of a human in block 720.When the second positional fingerprint(s) do not match the second storedfingerprint beyond the predetermined threshold of similarity, the methodmay end.

Examples of the present disclosure can be implemented according to atleast the following clauses:

Clause 1: A system comprising: one or more processors; one or morepositional sensors configured to communicate with the one or moreprocessors; a memory in communication with the one or more processorsand storing instructions that, when executed by the one or moreprocessors, are configured to cause the system to: receive first levelauthentication data from a first user; based on the first levelauthentication data, identify a first user device associated with thefirst user; determine whether a current location of the first userdevice is within a predetermined proximity of a first computing device;responsive to determining that the current location of the first userdevice is within the predetermined proximity of the first computingdevice, detect one or more objects within the predetermined proximity ofthe first computing device using the one or more positional sensors;determine that at least one of the one or more objects is associatedwith a human; responsive to the determination, trigger a securitymeasure; and transmit a an indication of the triggered security measureto the first computing device.

Clause 2: The system of clause 1, wherein the memory includesinstructions that, when executed by the one or more processors areconfigured to cause the system to: responsive to not determining thatthe current location of the first user device is within thepredetermined proximity of the first computing device, determine a firstpositional fingerprint associated with the first user; compare the firstpositional fingerprint to a stored fingerprint associated with the firstuser; responsive to the first positional fingerprint matching the storedfingerprint beyond a first predetermined threshold, confirm that thecurrent location of the first user device is within the predeterminedproximity of the first computing device.

Clause 3: The system of clause 2, wherein the first positionalfingerprint and the stored fingerprint each comprise a data typeselected from LIDAR fingerprint data, ultrasound fingerprint data,capacitive fingerprint data; resistive fingerprint data, radio-frequency(RF) fingerprint data, facial recognition fingerprint data, orcombinations thereof.

Clause 4: The system of clause 1, wherein the memory includesinstructions that, when executed by the one or more processors areconfigured to cause the system to: responsive to not determining thatthe current location of the first user device is within thepredetermined proximity of the first computing device, access storedactivity data associated with the first user; determine whether currentfirst user activity associated with the first user device matches storedactivity data associated with the first user; responsive to the currentactivity matching the stored activity data beyond a second predeterminedthreshold, confirm the current location of the first user device iswithin the predetermined proximity of the first computing device.

Clause 5: The system of clause 1, wherein determining that at least oneof the one or more objects is associated with a human further comprises,for each of the one or more objects: determining a second positionalfingerprint; comparing the second positional fingerprint to a storedfingerprint associated with a presence of a human; and determining thata second object is associated with the presence of a human based on thesecond positional fingerprint matching the stored second fingerprintbeyond a third predetermined threshold.

Clause 6: The system of clause 5, further comprising a trained machinelearning model configured to determine the presence of a human based onthe comparison of one or more second positional fingerprints to thestored second fingerprint.

Clause 7: The system of clause 1, wherein the security measurescomprises one or more actions selected from (i) causing a displayassociated with the first computing device to flash, (ii) causing thedisplay to temporarily display an empty screen, or (iii) causing thedisplay to identify a position associated with a presence of the humanwithin the predetermined proximity of the first computing device.

Clause 8: The system of clause 1, wherein an indication of the triggeredsecurity measure is additionally transmitted to the first user device,causing the first user device to provide an audible or vibrational alertto the first user.

Clause 9: A system comprising: one or more processors; a memory incommunication with the one or more processors and storing instructionsthat, when executed by the one or more processors, are configured tocause the system to: receive first level authentication data from afirst user; based on the first level authentication data, identify afirst user device associated with the first user; determine whether acurrent location is within a predefined area proximate a first computingdevice; responsive to determining that the current location of the firstuser device is within the predefined area proximate the first computingdevice, receive object data comprising one or more objects within thepredefined area; determine that at least one of the one or more objectsis associated with a human; and responsive to the determination, triggera security measure.

Clause 10: The system of clause 9, wherein the memory includesinstructions that, when executed by the one or more processors areconfigured to cause the system to: responsive to not determining thatthe current location of the first user device is within the predefinedarea proximate the first computing device, determine a first positionalfingerprint; compare the first positional fingerprint to a storedfingerprint associated with the first user device; responsive to thefirst positional fingerprint matching the stored fingerprint beyond afirst predetermined threshold, confirm that the current location of thefirst user device is within the predefined area.

Clause 11: The system of clause 10, wherein the first positionalfingerprint and the stored fingerprint each comprise a data typeselected from LIDAR fingerprint data, ultrasound fingerprint data,capacitive fingerprint data, resistive fingerprint data, radio-frequency(RF) fingerprint data, facial recognition fingerprint data, orcombinations thereof.

Clause 12: The system of clause 9, wherein the memory includesinstructions that, when executed by the one or more processors areconfigured to cause the system to: responsive to not determining thatthe current location of the first user device is within the predefinedarea proximate the first computing device, access stored activity dataassociated with the first user; determine whether current first useractivity associated with the first user device matches stored activitydata associated with the first user; responsive to the current firstuser activity matching the stored activity data beyond a secondpredetermined threshold, confirm that the current location of the firstuser device is within the predefined area.

Clause 13: The system of clause 9, wherein determining that at least oneof the one or more objects is associated with a human further comprises,for each of the one or more objects: determining a second positionalfingerprint; comparing the second positional fingerprint to a storedsecond fingerprint associated with a presence of a human; anddetermining that a second object is associated with the presence of ahuman based on the second positional fingerprint matching the storedsecond fingerprint beyond a third predetermined threshold.

Clause 14: The system of clause 13, further comprising a trained machinelearning model configured to determine the presence of a human based onthe comparison of one or more second positional fingerprints to thestored second fingerprint.

Clause 15: The system of clause 9, wherein the security measurecomprises one or more actions selected from (i) causing a displayassociated with the first computing device to flash, (ii) causing thedisplay to temporarily display an empty screen, or (iii) causing thedisplay to identify a position associated with a presence of the humanwithin the predefined area.

Clause 16: The system of clause 9, wherein the memory includesinstructions that, when executed by the one or more processors areconfigured to cause the system to: transmit instructions to the firstcomputing device, the instructions configured to cause the firstcomputing device to execute the security measure; and transmitinstructions to the first user device, the instructions configured tocause the first user device to provide an audible or vibrational alertto the first user.

Clause 17: A method comprising: receiving first level authenticationdata from a first user; based on the first level authentication data,identifying a first user device associated with the first user;determining whether a current location of the first user device iswithin a predetermined perimeter around a first computing device;responsive to determining that the current location of the first userdevice is within the predetermined perimeter around the first computingdevice, detecting, via one or more positional sensors positioned withinthe predetermined perimeter around the first computing device, one ormore objects within the predetermined perimeter around the firstcomputing device; determining that at least one of the one or moreobjects is associated with a human; responsive to the determination,triggering a security measure; transmitting an indication of thetriggered security measure to the first computing device; andtransmitting instructions to the first user device, the instructionsconfigured to cause the first user device to provide an audible orvibrational alert to the first user.

Clause 18: The method of clause 17, wherein the security measurecomprises one or more actions selected from (i) causing a displayassociated with the first computing device to flash, (ii) causing thedisplay to temporarily display an empty screen, or (iii) causing thedisplay to identify a position associated with a presence of the humanwithin the predetermined perimeter around the first computing device.

Clause 19: The method of clause 17, wherein determining that at leastone of the one or more objects is associated with a human furthercomprises, for each of the one or more objects: determining a positionalfingerprint; comparing the positional fingerprint to a storedfingerprint associated with a presence of a human; and determining thata second object is associated with the presence of a human based on thepositional fingerprint matching the stored fingerprint beyond apredetermined threshold.

Clause 20: The method of clause 19, wherein the positional fingerprintand the stored fingerprint each comprise a data type selected from LIDARfingerprint data, ultrasound fingerprint data, capacitive fingerprintdata, resistive fingerprint data, radio-frequency (RF) fingerprint data,facial recognition fingerprint data, or combinations thereof.

Exemplary Use Cases

A user may walk up to a computer interface 130 and insert a debit cardand/or enter a PIN associated with the user's account. In response theproximity detection device 120 may identify a first user deviceassociated with the first user. The proximity detection device 120 maydetermine whether a current location of the first user device (e.g.,user device 102) is within a predetermined proximity of the computerinterface 130, for example, by querying financial service providersystem 140 for locational data associated with user device 102. Inresponse to determining that the current location of the first userdevice (e.g., user device 102) is within the predetermined proximity ofcomputer interface 130, the system may detect one or more objects withinthe predetermined proximity of computer interface 130 using one or morepositional sensors (e.g., positional sensors 122). The system (e.g.,proximity detection device 120) may determine that at least one of theone or more detected objects is associated with a human, and inresponse, trigger a security measure, and transmit an indication of thetriggered security measure to the first computing device. For example,the triggered security measure may include causing a display associatedwith computer interface 130 to flash, causing the display to temporarilydisplay an empty screen, and/or causing the display to identify aposition associated with a presence of the human within thepredetermined proximity of the computer interface 130.

The features and other aspects and principles of the disclosedembodiments may be implemented in various environments. Suchenvironments and related applications may be specifically constructedfor performing the various processes and operations of the disclosedembodiments or they may include a general-purpose computer or computingplatform selectively activated or reconfigured by program code toprovide the necessary functionality. Further, the processes disclosedherein may be implemented by a suitable combination of hardware,software, and/or firmware. For example, the disclosed embodiments mayimplement general purpose machines configured to execute softwareprograms that perform processes consistent with the disclosedembodiments. Alternatively, the disclosed embodiments may implement aspecialized apparatus or system configured to execute software programsthat perform processes consistent with the disclosed embodiments.Furthermore, although some disclosed embodiments may be implemented bygeneral purpose machines as computer processing instructions, all or aportion of the functionality of the disclosed embodiments may beimplemented instead in dedicated electronics hardware.

The disclosed embodiments also relate to tangible and non-transitorycomputer readable media that include program instructions or programcode that, when executed by one or more processors, perform one or morecomputer-implemented operations. The program instructions or programcode may include specially designed and constructed instructions orcode, and/or instructions and code well-known and available to thosehaving ordinary skill in the computer software arts. For example, thedisclosed embodiments may execute high level and/or low-level softwareinstructions, such as machine code (e.g., such as that produced by acompiler) and/or high-level code that can be executed by a processorusing an interpreter.

The technology disclosed herein typically involves a high-level designeffort to construct a computational system that can appropriatelyprocess unpredictable data. Mathematical algorithms may be used asbuilding blocks for a framework, however certain implementations of thesystem may autonomously learn their own operation parameters, achievingbetter results, higher accuracy, fewer errors, fewer crashes, andgreater speed.

As used in this application, the terms “component,” “module,” “system,”“server,” “processor,” “memory,” and the like are intended to includeone or more computer-related units, such as but not limited to hardware,firmware, a combination of hardware and software, software, or softwarein execution. For example, a component may be, but is not limited tobeing, a process running on a processor, an object, an executable, athread of execution, a program, and/or a computer. By way ofillustration, both an application running on a computing device and thecomputing device can be a component. One or more components can residewithin a process and/or thread of execution and a component may belocalized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate by way of local and/or remote processessuch as in accordance with a signal having one or more data packets,such as data from one component interacting with another component in alocal system, distributed system, and/or across a network such as theInternet with other systems by way of the signal.

Certain embodiments and implementations of the disclosed technology aredescribed above with reference to block and flow diagrams of systems andmethods and/or computer program products according to exampleembodiments or implementations of the disclosed technology. It will beunderstood that one or more blocks of the block diagrams and flowdiagrams, and combinations of blocks in the block diagrams and flowdiagrams, respectively, can be implemented by computer-executableprogram instructions. Likewise, some blocks of the block diagrams andflow diagrams may not necessarily need to be performed in the orderpresented, may be repeated, or may not necessarily need to be performedat all, according to some embodiments or implementations of thedisclosed technology.

These computer-executable program instructions may be loaded onto ageneral-purpose computer, a special-purpose computer, a processor, orother programmable data processing apparatus to produce a particularmachine, such that the instructions that execute on the computer,processor, or other programmable data processing apparatus create meansfor implementing one or more functions specified in the flow diagramblock or blocks. These computer program instructions may also be storedin a computer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meansthat implement one or more functions specified in the flow diagram blockor blocks.

As an example, embodiments or implementations of the disclosedtechnology may provide for a computer program product, including acomputer-usable medium having a computer-readable program code orprogram instructions embodied therein, said computer-readable programcode adapted to be executed to implement one or more functions specifiedin the flow diagram block or blocks. Likewise, the computer programinstructions may be loaded onto a computer or other programmable dataprocessing apparatus to cause a series of operational elements or stepsto be performed on the computer or other programmable apparatus toproduce a computer-implemented process such that the instructions thatexecute on the computer or other programmable apparatus provide elementsor steps for implementing the functions specified in the flow diagramblock or blocks.

Accordingly, blocks of the block diagrams and flow diagrams supportcombinations of means for performing the specified functions,combinations of elements or steps for performing the specifiedfunctions, and program instruction means for performing the specifiedfunctions. It will also be understood that each block of the blockdiagrams and flow diagrams, and combinations of blocks in the blockdiagrams and flow diagrams, can be implemented by special-purpose,hardware-based computer systems that perform the specified functions,elements or steps, or combinations of special-purpose hardware andcomputer instructions.

Certain implementations of the disclosed technology described above withreference to user devices may include mobile computing devices. Thoseskilled in the art recognize that there are several categories of mobiledevices, generally known as portable computing devices that can run onbatteries but are not usually classified as laptops. For example, mobiledevices can include, but are not limited to portable computers, tabletPCs, internet tablets, PDAs, ultra-mobile PCs (UMPCs), wearable devices,and smart phones. Additionally, implementations of the disclosedtechnology can be utilized with internet of things (IoT) devices, smarttelevisions and media devices, appliances, automobiles, toys, and voicecommand devices, along with peripherals that interface with thesedevices.

In this description, numerous specific details have been set forth. Itis to be understood, however, that implementations of the disclosedtechnology may be practiced without these specific details. In otherinstances, well-known methods, structures and techniques have not beenshown in detail in order not to obscure an understanding of thisdescription. References to “one embodiment,” “an embodiment,” “someembodiments,” “example embodiment,” “various embodiments,” “oneimplementation,” “an implementation,” “example implementation,” “variousimplementations,” “some implementations,” etc., indicate that theimplementation(s) of the disclosed technology so described may include aparticular feature, structure, or characteristic, but not everyimplementation necessarily includes the particular feature, structure,or characteristic. Further, repeated use of the phrase “in oneimplementation” does not necessarily refer to the same implementation,although it may.

Throughout the specification and the claims, the following terms take atleast the meanings explicitly associated herein, unless the contextclearly dictates otherwise. The term “connected” means that onefunction, feature, structure, or characteristic is directly joined to orin communication with another function, feature, structure, orcharacteristic. The term “coupled” means that one function, feature,structure, or characteristic is directly or indirectly joined to or incommunication with another function, feature, structure, orcharacteristic. The term “or” is intended to mean an inclusive “or.”Further, the terms “a,” “an,” and “the” are intended to mean one or moreunless specified otherwise or clear from the context to be directed to asingular form. By “comprising” or “containing” or “including” is meantthat at least the named element, or method step is present in article ormethod, but does not exclude the presence of other elements or methodsteps, even if the other such elements or method steps have the samefunction as what is named.

It is to be understood that the mention of one or more method steps doesnot preclude the presence of additional method steps or interveningmethod steps between those steps expressly identified. Similarly, it isalso to be understood that the mention of one or more components in adevice or system does not preclude the presence of additional componentsor intervening components between those components expressly identified.

Although embodiments are described herein with respect to systems ormethods, it is contemplated that embodiments with identical orsubstantially similar features may alternatively be implemented assystems, methods and/or non-transitory computer-readable media.

As used herein, unless otherwise specified, the use of the ordinaladjectives “first,” “second,” “third,” etc., to describe a commonobject, merely indicates that different instances of like objects arebeing referred to, and is not intended to imply that the objects sodescribed must be in a given sequence, either temporally, spatially, inranking, or in any other manner.

While certain embodiments of this disclosure have been described inconnection with what is presently considered to be the most practicaland various embodiments, it is to be understood that this disclosure isnot to be limited to the disclosed embodiments, but on the contrary, isintended to cover various modifications and equivalent arrangementsincluded within the scope of the appended claims. Although specificterms are employed herein, they are used in a generic and descriptivesense only and not for purposes of limitation.

This written description uses examples to disclose certain embodimentsof the technology and also to enable any person skilled in the art topractice certain embodiments of this technology, including making andusing any apparatuses or systems and performing any incorporatedmethods. The patentable scope of certain embodiments of the technologyis defined in the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral language of the claims.

What is claimed is:
 1. A system comprising: one or more processors; oneor more positional sensors configured to communicate with the one ormore processors; a memory in communication with the one or moreprocessors and storing instructions that, when executed by the one ormore processors, are configured to cause the system to: receive firstlevel authentication data from a first user during a first user session;based on the first level authentication data, identify a first userdevice associated with the first user; determine whether a currentlocation of the first user device is within a predetermined proximity ofa first computing device; responsive to not determining that the currentlocation of the first user device is within the predetermined proximityof the first computing device: determine, using the one or morepositional sensors, whether the current location of the first userdevice is within the predetermined proximity of the first computingdevice; responsive to determining that the current location of the firstuser device is within the predetermined proximity of the first computingdevice or determining, using the one or more positional sensors, thatthe current location of the first user device is within thepredetermined proximity of the first computing device, detect one ormore objects within the predetermined proximity of the first computingdevice using the one or more positional sensors; determine that at leastone of the one or more objects is associated with a human; responsive tothe determination that at least one of the one or more objects isassociated with the human, trigger a security measure; and transmit anindication of the triggered security measure to the first computingdevice during the first user session.
 2. The system of claim 1, whereindetermining, using the one or more positional sensors, whether thecurrent location of the first user device is within the predeterminedproximity of the first computing device further comprises: determining afirst positional fingerprint associated with the first user; comparingthe first positional fingerprint to a stored fingerprint associated withthe first user; and matching the first positional fingerprint to thestored fingerprint beyond a first predetermined threshold.
 3. The systemof claim 2, wherein the first positional fingerprint and the storedfingerprint each comprise a data type selected from LIDAR fingerprintdata, ultrasound fingerprint data, capacitive fingerprint data,resistive fingerprint data, radio-frequency (RF) fingerprint data,facial recognition fingerprint data, or combinations thereof.
 4. Thesystem of claim 1, wherein the memory includes instructions that, whenexecuted by the one or more processors are configured to cause thesystem to: responsive to not determining that the current location ofthe first user device is within the predetermined proximity of the firstcomputing device, access stored activity data associated with the firstuser; determine whether current first user activity associated with thefirst user device matches stored activity data associated with the firstuser; responsive to the current first user activity matching the storedactivity data beyond a second predetermined threshold, confirm thecurrent location of the first user device is within the predeterminedproximity of the first computing device.
 5. The system of claim 1,wherein determining that at least one of the one or more objects isassociated with a human further comprises, for each of the one or moreobjects: determining a second positional fingerprint; comparing thesecond positional fingerprint to a stored second fingerprint associatedwith a presence of a human, and determining that a second object isassociated with the presence of a human based on the second positionalfingerprint matching the stored second fingerprint beyond a thirdpredetermined threshold.
 6. The system of claim 5, further comprising atrained machine learning model configured to determine the presence of ahuman based on the comparison of one or more second positionalfingerprints to the stored second fingerprint.
 7. The system of claim 1,wherein the security measure comprises one or more actions selected from(i) causing a display associated with the first computing device toflash, (ii) causing the display to temporarily display an empty screen,or (iii) causing the display to identify a position associated with apresence of the human within the predetermined proximity of the firstcomputing device.
 8. The system of claim 1, wherein an indication of thetriggered security measure is additionally transmitted to the first userdevice, causing the first user device to provide an audible orvibrational alert to the first user.
 9. The system of claim 1, whereinthe memory includes instructions that, when executed by the one or moreprocessors are configured to cause the system to: responsive todetermining, using the one or more positional sensors, that the currentlocation of the first user device is not within the predeterminedproximity of the first computing device, terminate the first usersession.
 10. A system comprising: one or more processors; a memory incommunication with the one or more processors and storing instructionsthat, when executed by the one or more processors, are configured tocause the system to: receive first level authentication data from afirst user; based on the first level authentication data, identify afirst user device associated with the first user; determine whether acurrent location of the first user device is within a predefined areaproximate a first computing device; responsive to not determining thatthe current location of the first user device is within the predefinedarea proximate the first computing device: determine a first positionalfingerprint; compare the first positional fingerprint to a storedfingerprint associated with the first user device; responsive to thefirst positional fingerprint matching the stored fingerprint beyond afirst predetermined threshold, determine the first user is within thepredefined area proximate the first computing device; responsive todetermining that the current location of the first user device is withinthe predefined area proximate the first computing device or determiningthat the first user is within the predefined area proximate the firstcomputing device, receive object data comprising one or more objectswithin the predefined area; determine that at least one of the one ormore objects is associated with a human; and responsive to thedetermination that at least one of the one or more objects is associatedwith the human, trigger a security measure.
 11. The system of claim 10,wherein the first positional fingerprint and the stored fingerprint eachcomprise a data type selected from LIDAR fingerprint data, ultrasoundfingerprint data, capacitive fingerprint data, resistive fingerprintdata, radio-frequency (RF) fingerprint data, facial recognitionfingerprint data, or combinations thereof.
 12. The system of claim 10,wherein the memory includes instructions that, when executed by the oneor more processors are configured to cause the system to: responsive tonot determining that the current location of the first user device iswithin the predefined area proximate the first computing device, accessstored activity data associated with the first user; determine whethercurrent first user activity associated with the first user devicematches stored activity data associated with the first user; responsiveto the current first user activity matching the stored activity databeyond a second predetermined threshold, confirm that the currentlocation of the first user device is within the predefined area.
 13. Thesystem of claim 10, wherein determining that at least one of the one ormore objects is associated with a human further comprises, for each ofthe one or more objects: determining a second positional fingerprint;comparing the second positional fingerprint to a stored secondfingerprint associated with a presence of a human; and determining thata second object is associated with the presence of a human based on thesecond positional fingerprint matching the stored second fingerprintbeyond a third predetermined threshold.
 14. The system of claim 13,further comprising a trained machine learning model configured todetermine the presence of a human based on the comparison of one or moresecond positional fingerprints to the stored second fingerprint.
 15. Thesystem of claim 10, wherein the security measure comprises one or moreactions selected from (i) causing a display associated with the firstcomputing device to flash, (ii) causing the display to temporarilydisplay an empty screen, or (iii) causing the display to identify aposition associated with a presence of the human within the predefinedarea.
 16. The system of claim 10, wherein the memory includesinstructions that, when executed by the one or more processors areconfigured to cause the system to: transmit instructions to the firstcomputing device, the instructions configured to cause the firstcomputing device to execute the security measure; and transmitinstructions to the first user device, the instructions configured tocause the first user device to provide an audible or vibrational alertto the first user.
 17. A method comprising: receiving first levelauthentication data from a first user; based on the first levelauthentication data, identifying a first user device associated with thefirst user; determining that a current location of the first user deviceis not within a predetermined perimeter around a first computing device;responsive to determining that the current location of the first userdevice is not within the predetermined perimeter around the firstcomputing device: determining, using one or more proximity sensors, thatthe current location of the first user device is within thepredetermined perimeter around the first computing device; responsive todetermining, using the one or more proximity sensors, that the currentlocation of the first user device is within the predetermined perimeteraround the first computing device, detecting, via the one or moreproximity sensors positioned within the predetermined perimeter aroundthe first computing device, one or more objects within the predeterminedperimeter around the first computing device; determining that at leastone of the one or more objects is associated with a human; responsive tothe determination that at least one of the one or more objects isassociated with the human, triggering a security measure; transmittingan indication of the triggered security measure to the first computingdevice; and transmitting instructions to the first user device, theinstructions configured to cause the first user device to provide anaudible or vibrational alert to the first user.
 18. The method of claim17, wherein the security measure comprises one or more actions selectedfrom (i) causing a display associated with the first computing device toflash, (ii) causing the display to temporarily display an empty screen,or (iii) causing the display to identify a position associated with apresence of the human within the predetermined perimeter around thefirst computing device.
 19. The method of claim 17, wherein determiningthat at least one of the one or more objects is associated with a humanfurther comprises, for each of the one or more objects: determining apositional fingerprint; comparing the positional fingerprint to a storedfingerprint associated with a presence of a human; and determining thata second object is associated with the presence of a human based on thepositional fingerprint matching the stored fingerprint beyond apredetermined threshold.
 20. The method of claim 19, wherein thepositional fingerprint and the stored fingerprint each comprise a datatype selected from LIDAR fingerprint data, ultrasound fingerprint data,capacitive fingerprint data, resistive fingerprint data, radio-frequency(RF) fingerprint data, facial recognition fingerprint data, orcombinations thereof.